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Title: A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
Authors: Zeng, N
Wang, Z
Li, Y
Du, M
Liu, X
Keywords: Lateral flow immunoassay
Constrained optimization
Extended Kalman filtering
Parameter estimation
Switching particle swarm optimization
Publication Date: 2012
Publisher: IEEE
Citation: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(2): 321 - 329, Mar-Apr 2012
Abstract: In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.
Description: This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEE
Sponsorship: This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016.
ISSN: 1545-5963
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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